app.py

import time
import os
import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer
import meilisearch

tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m', add_pooling_layer=False)
model.eval()

cuda_available = torch.cuda.is_available()
print(f"CUDA available: {cuda_available}")

meilisearch_client = meilisearch.Client("https://edge.meilisearch.com", os.environ["MEILISEARCH_KEY"])
meilisearch_index_name = "docs-embed"
meilisearch_index = meilisearch_client.index(meilisearch_index_name)

def search_embeddings(query_text):
 start_time_embedding = time.time()
 query_prefix = 'Represent this sentence for searching code documentation: '
 query_tokens = tokenizer(query_prefix + query_text, padding=True, truncation=True, return_tensors='pt', max_length=512)
 # step1: tokenizer the query
 with torch.no_grad():
 # Compute token embeddings
 query_embeddings = model(**query_tokens)[0][:, 0]
 # normalize embeddings
 query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
 document_embeddings_list = query_embeddings[0].tolist()
 elapsed_time_embedding = time.time() - start_time_embedding
 
 # step2: search meilisearch
 start_time_meilisearch = time.time()
 response = meilisearch_index.search(
 "", opt_params={"vector": document_embeddings_list, "hybrid": {"semanticRatio": 1.0}, "limit": 5, "attributesToRetrieve": ["text", "source", "library"]}
 )
 elapsed_time_meilisearch = time.time() - start_time_meilisearch
 hits = response["hits"]

 # step3: present the results in markdown
 md = f"Stats:\n\nembedding time: {elapsed_time_embedding:.2f}s\n\nmeilisearch time: {elapsed_time_meilisearch:.2f}s\n\n---\n\n"
 for hit in hits:
 text, source, library = hit["text"], hit["source"], hit["library"]
 source = f"[source](https://huggingface.co/docs/{library}/{source})"
 md += text + f"\n\n{source}\n\n---\n\n"

 return md


demo = gr.Interface(
 fn=search_embeddings,
 inputs=gr.Textbox(label="enter your query", placeholder="Type Markdown here...", lines=10),
 outputs=gr.Markdown(),
 title="HF Docs Emebddings Explorer",
 allow_flagging="never"
)

if __name__ == "__main__":
 demo.launch()